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Record W1965616103 · doi:10.4319/lo.2011.56.3.0802

Inter‐annual changes in prey fields trigger different foraging tactics in a large marine predator

2011· article· en· W1965616103 on OpenAlexaff
Stefan Garthe, William A. Montevecchi, Gail K. Davoren

Bibliographic record

VenueLimnology and Oceanography · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of ManitobaMemorial University of Newfoundland
Fundersnot available
KeywordsForagingPelagic zonePredationCapelinBiologyEcologyFisheryPredatorRange (aeronautics)ForageForage fishPiscivore

Abstract

fetched live from OpenAlex

We report on inter‐annual comparisons of the foraging behavior of Global Positioning System–equipped chick‐rearing northern gannets ( Morus bassanus ) in the western Atlantic during years with contrasting oceanographic and prey conditions. We hypothesized that the predators would modify their foraging tactics when small fishes (capelin [ Mallotus villosus ]) and large pelagic fishes (mackerel, saury) varied in inter‐annual abundances. We predicted differences in (1) diving behavior, (2) spatial, and (3) temporal patterning of foraging behavior. Predictions 1 and 2 were supported, prediction 3 rejected. Dives were significantly deeper (4.3 ± 0.4 vs. 2.7 ± 0.3 m) and longer (10.1 ± 1.0 vs. 5.0 ± 0.2 s), and more U‐shaped dives (dives where birds stayed at more or less one depth) were performed (52% ± 7% vs. 7% ± 2%) in the year with higher abundance of forage fishes. Flight patterns exhibited remarkable spatial and geographic differences: gannets flew significantly (17%) more and foraging ranges were about twice as long when they pursued large pelagic fishes (mean = 122 ± 81 km vs. 62 ± 12 km). The 95% kernel feeding range was 34 times larger when large pelagic fishes were available. Yet foraging trip durations were not different between years. Inter‐annual variation in foraging tactics by the same species at the same colony in successive years was strongly related to prey availability, showing that spatial foraging parameters can be determined largely by ocean and prey conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.230
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations66
Published2011
Admission routes1
Has abstractyes

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